With the development of large-scale regional interconnected power grids, calculations such as power flow calculation, node voltage calculation, transient calculation and reactive optimization calculation in electric power system put forward a higher requirement for calculating speed. When computing scale reaches a certain degree, the calculating time becomes so long that it's hard to get the optimal result with existing computing methods. The Block Bordered Diagonal Form (BBDF) model is widely applied in many aspects of electric power system computing. Among the methods for solving BBDF model, the most common method is decomposition-coordination parallel algorithm. Currently, running in the single computer or multi-computer with rough scheduling methods is adopted commonly, which cause disadvantages such as long calculating time and poor efficiency.
The cloud computing data center is a new kind of internet computing pattern. As the technique of virtualization was presented, Virtual Machines (VMs) migration and consolidation technologies provide feasible methods for solving complex electric power system computing. In data center, great number of Physical Machines (PMs), i.e. computing servers, provide huge computing capability. For complex electric power system computing problem, it can be decomposed into many distributed small tasks, and then each of tasks map to VMs and is placed in PMs. By using this kind of calculating method, the complex calculating time will be greatly reduced.
Considering the process of parallel computing in data center, energy consumption is as important as computational acceleration. With the wide application of cloud computing and big data, energy consumption of data center becomes tremendously large. In 2001, the worldwide energy consumption of data center had reached 6358 hundred million kilowatt-hour (568 hundred million kilowatt-hour in China); and in 2012, the energy consumption increased to 7202 hundred million kilowatt-hour (664 hundred million kilowatt-hour in China). Therefore, only special calculation model and mapping methods with energy efficiency can be accepted and widely applied in data center. The present invention proposes an energy-efficient method to map BBDF model and decomposition-coordination parallel calculation into cloud computing data center.